Until now, there has been a need to control apparatuses in accordance with presence or absence of persons for energy saving. To determine the presence or absence of a person, person sensors using infrared rays, for example, are often used. A person sensor using infrared rays, however, cannot respond to a stationary person, nor determine the position of a person in the sensor coverage area even if the person is moving. Detecting of a person as well as the person's position, therefore, by using images taken by a camera has been conducted. For example, there are:
(1) Specific Object Detection Method by statistical learning of the feature quantities of images (image feature quantity),
(2) Background Difference Method by using the difference from the background image, and
(3) Inter-Frame Difference Method by using the difference from the previous image.
Among the typical methods of (1) Specific Object Detection Method is AdaBoost algorithm using Haar-Like feature or HOG (Histograms of Oriented Gradients) feature, which is mainly used for detection of a human face or a person. This method is effective for an object with rich image feature quantity, but detection of an object with poor feature quantity is difficult Because an image only of human heads and shoulders, for example, taken from right over them by a camera on the ceiling does not have unique feature quantity such as a human face and the shape of a person, failure in detecting a person or frequent erroneous detection happens.
Background Difference Method (2) can accurately extract the position of a person by calculating the difference between an image on which no person is photographed, which is used as the background image, and an image on which a person is photographed. However, this method is known to be weak against change in surrounding illuminance. Therefore, a method to moderately update the background image to meet the external environment has been considered. However, in the environment such as an office and a factory where comings and goings of people or on and off of lights are frequent, it is difficult to choose the optimum background image.
Inter-Frame Difference Method (3) can detect a moving object by calculating the difference between the previous image and the current image in time series, and is also relatively robust against the change in surrounding illuminance. However, because an object that does not move cannot be detected, a stationary person cannot be also detected.
Conventional techniques of the Inter-Frame Difference Method to detect a stationary object include a tracking method which tracks a moving object in an image to determine it to stop at the position where its movement has disappeared.
A problem with the tracking method is that, when plural objects which move (moving objects) exist, they cannot be tracked in the image, and as the result, whether they still exist at the positions where they stopped cannot be determined.
Furthermore, because there is a case where a moving object is not a person (for example, a chair with casters has moved), it is difficult to detect only a stationary person.
To cope with these, as a method to detect a stationary person by the tracking method using images, there is a technique to determine a stationary object by comparing the image feature quantities of image regions of stationary objects stored in advance with the image feature quantity of a specific object (for example, Patent Document 1).